Archiving of topmost ranked answers of a cognitive search

ABSTRACT

A method for archiving of documents of a query against a cognitive system can be provided. The cognitive system comprises at least a cognitive engine, several stored documents, and a learned model. The method comprises determining a plurality of evidence fragments, a related first list of documents and related metadata. The method also comprises removing a document from the stored documents, redetermining as second result a second list of documents, comparing the first and second list of documents, and upon determining identical documents in the compared first and second list of documents up to a confidence cliff, removing another document. Furthermore, the method comprises repeating the steps of removing, redetermining, and comparing until the first list of documents and the second list of documents differ above the confidence cliff and storing metadata of the documents of the first list, the plurality of evidence fragments, and the first query.

BACKGROUND

The present disclosure relates generally to re-traceability of answersof a cognitive engine, and more specifically, to improving the archivingof topmost ranked documents found during a query using a cognitivesystem.

Currently, enterprises look for ways to improve their decision-makingand customer management process—among others—by using cognitive systems.One implementation option for cognitive systems is based on usingnatural language as an input to generate one or more answers, (possiblyusing a weighting factor to rate the confidence of an answer), to aquestion. However, typically, cognitive systems are a sort of black-box.Thus, it is difficult to understand, retrace, and/or reproduce aspecific answer at a later time. One reason for this can be that theknowledge base has been changed between the first and a second askingand answering of a question. Another reason could be the learned modelshave changed. However, to develop trust and confidence in these kinds ofsystems, it is important to know the facts used to determine theoriginal answer.

SUMMARY

Aspects of the present disclosure are directed toward a method forarchiving topmost ranked documents, the method comprising receiving afirst query into a cognitive system, where the cognitive systemcomprises a cognitive engine, a plurality of stored documents, and arelated learned model. The method can further comprise determining afirst result of the first query against the cognitive system based onthe related learned model, where the first result comprises a pluralityof evidence fragments and ranking the plurality of evidence fragments.The method can further comprise determining for the first query a firstlist of documents comprising documents selected from the plurality ofstored documents, where respective documents in the first list ofdocuments relate to the plurality of evidence fragments. The method canfurther comprise determining metadata of the documents in the first listof documents and removing a first document from the plurality of storeddocuments, where the first document is an element of the first list ofdocuments, and where the first document does not relate to topmostranked evidence fragments. The method can further comprise redetermininga second result comprising a second list of documents of the first queryof the plurality of stored documents without the first document, wherethe documents in the second list relate to the plurality of evidencefragments. The method can further comprise determining a confidencecliff and comparing the first list of documents with the second list ofdocuments. The method can further comprise, in response to determiningidentical documents in the first list of documents and the second listof documents up to the confidence cliff, removing a second document fromthe plurality of stored documents, where the second document is anelement of the first list of documents and the second list of documents,and where the second document does not relate to the topmost rankedevidence fragments. The method can further comprise storing the metadataof respective documents of the first list of documents, the plurality ofevidence fragments, and the first query.

Further aspects of the present disclosure are directed toward a systemand computer program product having similar characteristics as themethod discussed above. The present Summary is not intended toillustrate each aspect of, every implementation of, and/or everyembodiment of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings included in the present application are incorporated into,and form part of, the specification. They illustrate embodiments of thepresent disclosure and, along with the description, serve to explain theprinciples of the disclosure. The drawings are only illustrative ofcertain embodiments and do not limit the disclosure.

FIG. 1 shows a flowchart of an example method for archiving of topmostranked documents of a result of a query against a cognitive system, inaccordance with embodiments of the present disclosure.

FIG. 2 shows a block diagram illustrating high-level components of acognitive engine, in accordance with embodiments of the presentdisclosure.

FIG. 3 shows a block diagram of relevant corpus data, in accordance withembodiments of the present disclosure.

FIG. 4 shows a block diagram of question specific data/documents, inaccordance with embodiments of the present disclosure.

FIG. 5 shows a table and graph illustrating the concept of theconfidence cliff, in accordance with embodiments of the presentdisclosure.

FIG. 6 shows a flowchart of an example method for outputting an answer,in accordance with embodiments of the present disclosure.

FIG. 7 shows a simplified block diagram of a system for archiving oftopmost ranked documents of a result of a query against a cognitivesystem, in accordance with embodiments of the present disclosure.

FIG. 8 shows a block diagram of a computing system capable of executingprogram code related to aspects of the present disclosure.

While the present disclosure is amenable to various modifications andalternative forms, specifics thereof have been shown by way of examplein the drawings and will be described in detail. It should beunderstood, however, that the intention is not to limit the presentdisclosure to the particular embodiments described. On the contrary, theintention is to cover all modifications, equivalents, and alternativesfalling within the spirit and scope of the present disclosure.

DETAILED DESCRIPTION

Cognitive systems are gaining widespread use across all aspects oftechnology. One implementation option for cognitive systems is based onusing natural language as an input to generate one or more answers,(possibly using a weighting factor to rate the confidence of an answer),to a question. However, typically, cognitive systems are a sort ofblack-box. Thus, it is difficult to understand and retrace or toreproduce a specific answer at a later time. One reason for this can bethat the knowledge base may have changed between the first and a secondasking of a question. Another reason is the learned models may havechanged. However, to develop trust and confidence in these kinds ofsystems, it is useful to know the facts, processes, and models used todetermine the previous answer. Traditional systems are hindered in theability to retrace or replicate an answer because of the large amount ofdata traditionally needed to retrieve and/or replicate an answer.

Aspects of the present disclosure can reduce an amount of storagerequired in order to retrace and reproduce the answer of a cognitivesystem at a later point in time. Some embodiments of this disclosure canbe effective even if the knowledge body—i.e., the corpus—and the learnedmodels have been changed between the first answer and a second answerfor the same question. Thus, it may no longer be required to store thecomplete corpus—i.e., all documents of the knowledge body—in order toprovide re-traceability of an answer that was generated by a cognitiveengine. In some embodiments, the amount of storage saved can range froma factor of about 10 to about 100, while in other embodiments, the datacompression can be a factor of 1000 or more. Thus, only 1/1000 (or less)of the original knowledge body and learned model data with the requiredevidence fragments can reproduce the original answer with sufficientaccuracy.

In the context of this description, the following conventions, termsand/or expressions can be used:

The term ‘query’ can denote a question posed to a cognitive engine.Typically, and in the classical sense of information management, a querycan be an SQL statement (structured query language). However, in thecontext of cognitive computing, and this disclosure, a query can also bea question in a natural language.

The term ‘document(s)’ can denote any data, in text form, asspeech/voice, music/sound, image, video, or any other human ormachine-readable stored information. The sum of documents together canbe denoted as a data corpus (or knowledge corpus data, or corpus) andtogether with a learned model (or more) it can be denoted as groundtruth for the cognitive engine. This can basically represent theknowledge a cognitive system has, i.e., the knowledge base. Thedocuments can typically relate to a specific subject-matter.Additionally, in the context of the here presented disclosure, the termdocument can be used synonymously for the term evidence fact. Although,a typical artificial intelligence machine can differentiate betweendocuments and evidence facts.

The term ‘evidence fragments’ can denote a small fragment of data out ofone of the documents. Evidence fragments can be ranked according totheir probability of building or supporting an answer to the questionposed to the cognitive system. Additionally, an evidence fragment canalso be a conclusion based on a part of a document.

The term ‘machine learning’ is known to be a subset of artificialintelligence and can enable a computer system to “learn” (i.e.,progressively improve performance on a specific task) with data, withoutbeing explicitly programmed. Thus, the term ‘learned model’ can denote amachine learning model or response system enabling a computer system torespond to an input data pattern based on having been trained withtraining data and a defined outcome relating to the training data.

The term ‘cognitive system’ can denote a computing system used forreasoning, based on a corpus of facts and interrelationships betweenfacts stored as—almost completely—human understandable documents.Cognitive computing performed by a cognitive system can also be denotedas artificial intelligence (AI, also machine intelligence) which can bea sort of intelligence demonstrated by machines, in contrast to thenatural intelligence displayed by humans and other animals. In computerscience, AI research is defined as the study of “intelligent agents”:any device that perceives its environment and takes actions thatmaximize its chance of successfully achieving its goals. Colloquially,the term “artificial intelligence” can be applied when a machine mimics“cognitive” functions that humans associate with other human minds, suchas “learning” and “problem solving”.

The term ‘cognitive engine’ can denote a system in the sense of theabove definition of a cognitive system. It can decompose a question in anatural language, can access a large plurality of documents, which canbe stored separate from the cognitive engine, can use a search engine,filtering agents, and a scoring system for potential answers andeventually a speech synthesis element in order to articulate an answerto the question.

The term ‘topmost ranked evidence fragment’ can denote those evidencefragments having the highest confidence level or confidence level valueand, consequently, relate to the documents having the highest confidencelevels. In some embodiments, the confidence level can be expressed as aprobability factor or a type of score. The same can be valid forevidence fragments.

The term ‘topmost ranked documents of a result’ can denote documentsthat have the highest probability that they were relied upon by thecognitive machine to determine the answer. Aspects of this disclosurerecognize a sequence of documents exist that can underlie an answer to acognitive machine. The answer can be based on, or can be one of, theevidence fragments for which a corresponding document exists. Typically,the documents relating to the highest ranked—most probable—evidencefragments can be the topmost ranked documents.

The term ‘result’ can denote a list of documents of the question posedto the cognitive system. The result can be a ranked list of documents inwhich the topmost ranked document can relate to the topmost evidencefragment. Typically, the ranking of the evidence fragment—i.e., also thedocuments—can be performed according to a confidence value or score.

The term ‘optimized archiving’ or ‘archiving’ can denote that only aportion of the knowledge body, evidence fragments, the cognitive enginecode and the trained or learned models need to be stored. This cansignificantly reduce the amount of required storage, while at the sametime, the reasoning process, the used data, and other dependencies canbe comprehendible at a time after the original query.

The term ‘confidence cliff’ can denote to a document in the result listbeing determined either statically or dynamically. Examples of a staticdetermination can be a simple rank in the list of documents or apredefined confidence level. A dynamic determination of the confidencecliff can use the 1^(st) derivative of the confidence levels.

Some embodiments of the present disclosure comprise acomputer-implemented method for archiving of topmost ranked documents ofa result of a query against a cognitive system. Or said differently,embodiments of the present disclosure can rank the plethora of documentsused by a cognitive system in answering a question. This subset ofdocuments, selected from the documents in the corpus, can be stored.This will provide a means to understand how the answer was reached whileonly storing a small percentage of the information used to obtain theanswer.

Aspects of the present disclosure used for archiving of topmost rankeddocuments can offer multiple advantages and technical effects.

Aspects of the present disclosure are based on removing documentsrelated to evidence fragments from which the answer is generatedhowever, removing the documents will not change the answer or lower theconfidence of the answer below a confidence threshold. Embodiments ofthe present disclosure recognize it would be useful if the cognitiveengine using the learned model would be agnostic against missingdocuments in the knowledge body. Such can be achieved. By adjusting aconfidence cliff manually or automatically using different methods basedon the required accuracy of the answer, the compression factor for theoriginal corpus can be adjusted individually.

In some embodiments, the result of the query can be regenerated with thereduced number of documents. This can be done to ensure the answerremains the same without saving the entire corpus.

In the following, additional embodiments of the present disclosure willbe described.

According to some embodiments, the learned model can be agnostic to amissing document in the plurality of stored documents. Thus, it may beirrelevant that a document is deleted in the archived version of theknowledge body—i.e., the ground truth of the cognitive system. This canbe a useful feature because the archived version of the answer and thesurrounding context—i.e., topmost ranked evidence fragments as well astopmost ranked documents—may not have the full ground truth if comparedto the version at the time of the original result and answer. Thus, inorder to have a reproducibility or re-traceability of the answer/result,the cognitive engine can be enabled to function correctly with only aportion of the original number of documents.

According to one embodiment, the metadata can comprise one or morepieces of information related to a document including but not limited toname, at least one document author, a document source, a documentpublishing date, at least one of document bibliographic data, anInternational Standard Book Number, i.e., ISBN, a web-link, e.g., inform of an HTTP address (HyperText Transfer Protocol). Thus, anymetadata describing the document or being helpful in re-accessing thedocument can be used and stored instead of the original documentresulting in a substantial reduction in the amount of data to be storedin order to access the original document. It can also be noted thatdocument types can come in any form like text, voice, image, video, PDF(portable document format), and the like. Basically, any machinereadable and/or interpretable document format can be used.

According to one embodiment, storing the metadata can also comprisestoring information about at least one learned model. This can comprisetraining data and desired results/answers, the corpus data at the timeof the training, the number of training runs, and similar datadescribing the status of the related machine learning model.Additionally, storing can also comprise a version number of thecognitive engine version at the time of the first query. Thus, thecontext of the first query can easily be reproduced.

According to some embodiments, storing the metadata can also comprisestoring only the topmost evidence fragments of the plurality of evidencefragments, i.e., those evidence fragments having the highest confidencevalue. Thus, only the topmost 1% or up to e.g. 10% of the evidencefragments can be stored.

According to some embodiments, the confidence cliff can be expressed asa predefined number of documents. This number can be the document afterwhich the confidence level of the document relating to the topmostevidence fragments show a significant change, e.g., at the point wherethe absolute value of the derivative of the confidence level can have alocal maximum. However, other rules than the above-described dynamicdetermination for the confidence cliff can be defined. E.g., a staticvalue can be used, e.g., the 10^(th) document, a number of evidencefragments, or any other predefined number.

According to some embodiments, the confidence cliff can relate to adocument in the first list with a predefined confidence level. This canalso be seen as a static determination approach which can require lesscomputational effort if compared to a dynamic determination of theconfidence cliff value.

According to some embodiments, the confidence cliff can be determined bydetermining a confidence level polynomial using absolute values ofconfidence levels of documents of the first list and setting theconfidence cliff to the document at or just after the polynomial's firstlocal maximum. Thereby, it can be assumed that the derivative of theconfidence level (or confidence level value) of the first document, forexample, the one relating to the highest ranked evidence fragment, isequal to zero. This way, the local maximum can be determined using knownalgorithms.

According to some embodiments, a list of answers relating to the firstquery can be determined by the cognitive engine together with a scoringof the answers. The scoring can also be interpreted as the confidencelevel or confidence level value.

In some embodiments, the cognitive system can comprise a cognitiveengine, a plurality of stored documents and at least one related learnedmodel. In such embodiments, the method can further comprise, determiningfor the result of a first query against the cognitive system based onthe at least one related learned model, a plurality of evidencefragments; and determining, as the result of the first query, a firstlist of documents out of the plurality of stored documents. Thereby, thedocuments in the first list may relate to the plurality of determinedevidence fragments.

Furthermore, the method may comprise determining metadata of the firstlist of documents, removing a document from the stored documents, wherethe removed document is an element of the resulting first list ofdocuments, and where the removed document does not relate to topmostranked evidence fragments, and redetermining as a second result a secondlist of documents of the first query out of the plurality of storeddocuments without the removed document, wherein the documents in thefirst and second list relate to the plurality of determined evidencefragments.

Additionally, the method may comprise comparing the first list ofdocuments with the second list of documents, and upon determiningidentical documents in the compared first and second list of documentsup to a confidence cliff, removing another document from the storeddocuments, wherein the removed other document is an element of theresulting list of documents, and wherein the other document does notrelate to the topmost ranked evidence fragments.

In some embodiments, the method further comprises, while said first listof documents and said second list of documents are equal up to saidconfidence cliff and documents remain in said stored documents,repeating the steps of removing another document, redetermining, andcomparing.

According to another aspect of the present disclosure, a related systemfor archiving of topmost ranked documents of a result of a query againsta cognitive system may be provided.

Furthermore, embodiments may take the form of a related computer programproduct, accessible from a computer-usable or computer-readable mediumproviding program code for use, by, or in connection with, a computer orany instruction execution system. For the purpose of this description, acomputer-usable or computer-readable medium may be any apparatus thatmay contain hardware and/or software for storing, communicating,propagating or transporting the program for use, by, or in connection,with the instruction execution system, apparatus, or device.

In the following, a detailed description of the figures will be given.All instructions in the figures are schematic. Firstly, a flowchart ofsome embodiments of the disclosed computer-implemented method forarchiving of topmost ranked documents of a result of a query against acognitive system is given. Afterwards, additional embodiments, as wellas embodiments of the system for archiving of topmost ranked documentsof a result of a query against a cognitive system, will be described.

FIG. 1 is a block diagram of an embodiment of the computer-implementedmethod 100 for archiving of topmost ranked documents of a result of aquery—here, a question expressed in a natural language—against acognitive system. The cognitive system can comprise at least a cognitiveengine, a plurality of stored documents (i.e. the corpus for specificsubject-matter) and at least one related learned model. Together, theplurality of stored documents as well as the trained machine learningmodel can define the ground truth of the cognitive engine.

Step 102 of method 100 comprises determining a plurality of evidencefragments for the result of a first query against the cognitive systembased on the at least one related learned model. In some embodiments theplurality of evidence fragments can be ranked according to a confidencelevel.

Step 104 of method 100 comprises determining a first list of documents,as the result of the first query against a first list of documents outof the plurality of stored documents, where the documents in the firstlist relate to the plurality of determined evidence fragments. The firstlist typically comprises at least a document number, a document title,and a confidence level.

Step 106 of method 100 comprises determining metadata of the documentsof the first list of documents with which the documents can beidentified without needing the document itself.

Step 108 of method 100 comprises removing at least one document from thestored documents. In some embodiments, at least one document is removedfrom the stored documents by setting a removal flag, so that thedocument will not be used as part of the corpus during subsequentqueries. Thereby, the removed document is an element of the resultingfirst list of documents, where the removed document does not relate totopmost ranked evidence fragments. Thus, the document can appear in theresulting first list, but in a subordinate position, in particular,below the confidence cliff.

Step 110 of method 100 comprises redetermining as a second result asecond list of documents of the first query out of the plurality ofstored documents without the removed document, where the documents inthe second list relate to the plurality of determined evidencefragments.

Step 112 of method 100 comprises comparing the first list of documentsto the second list of documents. Typically, this is done list element bylist element, for example, row by row. Step 112 can further includedetermining identical documents in the compared first and second list ofdocuments up to a confidence cliff.

Step 114 of method 100 comprises removing another document from thestored documents. Thereby, the removed other document is an element ofthe resulting list of documents, and the other document does not relateto the topmost ranked evidence fragment. Or said differently, theremoved document is below the threshold confidence and both lists areequal up to the threshold confidence.

Step 116 of method 100 comprises redetermining the second list ofdocuments, comparing the redetermined second list of documents againstthe first list of documents, where some documents have been removed, andremoving another document. In some embodiments, this process is repeatedas long as the first list of documents, and the second list of documentsare equal (e.g., equivalent, identical, similar, etc.) up to theconfidence cliff. In other words, as long as the lists of documents upto the confidence cliff are equal, the loop continues.

Step 118 of method 100 comprises storing the metadata of the documentsof the first list (i.e. the result in a shortened form), the pluralityof evidence fragments, and the first query. It is noted that instead ofstoring the complete documents, only metadata of the documents isstored. This reduces the amount of stored data significantly and enablesa user to reproduce or at least comprehend the answer produced by thecognitive engine at a later point in time after the first query wasposed to the cognitive system.

FIG. 2 generally labeled 200 shows a block diagram illustratinghigh-level components of a cognitive engine relevant for the proposedconcept. A person having ordinary skill in the art will understand thatthe shown block diagram is consistent with the general mode of operationof cognitive engines: A linguistic preprocessor (often also receiving acategory as basis for a question) receives a natural language question.After some preprocessing (relationship analysis, focus analysis, lexicalmapping, tokenizing), the question can be separated in partialquestions. The lexical mapping can be performed using a plurality ofdifferent data sources. The partial questions can be used to generate aplurality of potential answers—denoted as candidates or hypotheses—usingone or more search engines. The hypotheses are then evaluated andassessed using a plurality of parallel working agents or expert systems(e.g., trained AI models). As a result of the agent evaluation, a largenumber of evidence fragments can be generated (e.g., 100 to 250hypotheses can result in, e.g., 100,000 evidence fragments). A list ofanswers is generated with related weight factors or relevance factors(also denoted as confidence factors). Finally, and optionally, a speechsynthesis can be used to generate a resulting answer in natural languagebased on the potential answer having the highest confidence factor.

Coming back to FIG. 2, based on the question 202, a plurality ofhypotheses or hypothesis facts 204 are generated which relate to alarger plurality of evidence fragments 206. The evidence fragments 206relate to some of the plurality of answer sources 208, i.e., a totalplurality of documents. The total number of evidence fragments 206 canthen be reduced to useful evidence sources 210 which relate to a reducednumber of documents. In this context, learned models 212 are applied asexpert system or agents (see above), and the answer 214 can be theevidence fragment with the topmost confidence level, or a list ofdocuments with the highest probabilities/confidence levels.

FIG. 3 shows a block diagram 300 of relevant corpus data 302 that can bestored in order to render an answer traceable after a period of timeafter the initial answer was generated. During the period of time, thecorpus data—e.g., the underlying document bases—used for the cognitiveengine as well as a version of the cognitive engine and/or trainedmodels can change. However, in order to understand at a later point intime why a specific answer was generated for a specific question, thecorpus data comprising answer sources 208—i.e., documents relevant forthe answer—evidence sources 210 (which can be seen as identical to theanswer sources, however, in some implementations one can differentiatebetween documents and facts derived from the documents), one or morelearned models 212 and the answer(s) 214 with the highest confidencelevels beside the original question 202 need to be stored. It is notedthat the most probable answer is identical to the evidence fragmenthaving the highest probability to be correct, i.e., the highestconfidence level.

Using only metadata for the documents—e.g., document name, documentauthor(s), document source(s), publishing date, bibliography data, anISBN (International Standard Book Number) and similar—will reduce theamount of data to be stored as corpus data significantly. It is alsonoted that the documents can be available not only as simple textdocuments, but also in PDF format, HTML format, as image, sound, video,or any other form interpretable by a cognitive engine. Additionally,data compression techniques can be applied to further reduce the datavolume to be stored.

Summarized, the answer sources 208 representing the totality ofdocuments from which the evidence sources 210 are derived—i.e., thosedocuments supporting the answer—as well as the learned models 212represent corpus data 302 that includes slow changing data which can bechanged after a plurality of question/answer pairs, e.g., if newdocuments enter the document base. In some embodiments, this data corpuscan be stored once for a plurality of question/answer pairs as long asthe documents base is not changed.

In contrast to FIG. 3, FIG. 4 shows a block diagram 400 of questionspecific data/documents to be stored. The question specific data changefrom question to question. The general approach—i.e., the approach toreduce the required amount of data—can be described as follows: In stepA the cognitive engine performs the original or first run and stores the“answer candidate” 402 (i.e., also known as hypothesis) and related“evidence fragments” 404 of this run for a question 202. In step B,using a grade (e.g., a derivative) of the curve regarding confidencelevels of the documents relating to the evidence fragments for a 1^(st),2^(nd) or 3^(rd) confidence cliff, the number of answer sources isreduced according to the 1^(st), 2^(nd) or 3^(rd) confidence cliff, andan answer 212 is generated. For the concept of the confidence cliffrefer to FIG. 5.

In a next run, step C, a re-run of the answer generation is performed,however, now with the reduced answer sources (i.e., reduced number ofdocuments) which are intermediately stored answers of the answercandidates together with related evidence fragments of this run.

Then, in step D, the results of step A and step B are compared. If theresulting data differences, i.e., differences in documents between thedocument lists, are below a threshold, the data to be stored isacceptably small and the procedure stops.

If the comparison shows that the results of step A and step B i.e., theresulting documents, differ by more than the threshold, then, a nextstep E is performed: re-running the above sequence as often as requiredto meet the defined threshold.

This way, the minimum possible number of documents 302 and evidencefragments 304 are stored for a given question 202 (i.e., query) and arelated answer 212. It can also be noted that only the topmost evidencefragments will be stored, e.g. the top 1% up to an exemplary limit of10% of all evidence fragments.

FIG. 5 is useful in explaining the concept of the above-mentionedconfidence cliff. Out of the confidence levels of the evidencefragments, a confidence value of related documents is generated, asshown in table 500 (only the 1^(st) 10 documents are shown,exemplarily). Besides the confidence value of a document, the number ofthe document (doc-id), and a title of the documents are listed, asexamples, in FIG. 5. Such a table can be a partial outcome of a run ofthe cognitive engine. However, the data shown are enough to explain theconcept of the confidence cliff.

Besides the confidence values, a grade (i.e., 1^(st) derivative) isdetermined at each confidence value. The table is sorted according tothe confidence values.

The bottom part of FIG. 5 shows a diagram of the curve 502 of theconfidence value and the curve of 1^(st) derivative 504. The x-axisshows a running number of documents (not shown in the table above). They-axis shows the numerical values of the confidence values and therelated 1^(st) derivative. The dashed lines from the table to thediagram show the relationship between the “confidence” column and“grade” column to the curves 502 and 504.

As can be seen, the 1^(st) derivative (i.e., grade) in the curve of theconfidence values shows a peak at the point of the 2^(nd) document. Thissuggests that the document with number 3 and higher do not seem to berelevant for the evidence fragments, i.e., the related documents, andthus for the answer (which is the first evidence fragment). Hence, aconfidence cliff can be set to document number 2. Thereby reducing thenumber of documents for a re-run of the answer generation (e.g., seeFIG. 4) by removing a document having a higher number in the sortingaccording to the table 500 than the document defining the confidencecliff.

It can also be noted that the confidence cliff can be defined in astatic way, e.g., by defining a minimum number of documents or inanother way.

FIG. 6 shows a flowchart 600 of an example method for outputting ananswer.

Step 602 of process 600 comprises receiving a question to the cognitivesystem. Step 604 of process 600 comprises a first round ofdetermination. In some embodiments, the first round of determinationincludes determining a plurality of evidence fragments and determiningthe documents that correspond to each evidence fragment (e.g., see FIG.1, 102, 104). Step 606 of process 600 comprises compiling a first listof documents. In some embodiments, the documents are numbered A₁ toA_(n). In some embodiments, there can be a 1:1 relationship between theevidence fragments and the documents or in some embodiments there can bean n:m relationship.

Step 608 of process 600 comprises calculating a confidence cliff. Insome embodiments, a confidence cliff is calculated using the conceptaccording to FIG. 5. In some embodiments, a document A_(k) marks thecliff level.

Step 610 of process 600 comprises making one or more documentsunavailable for a re-run of the cognitive engine. In some embodiments,the one or more documents made unavailable are below the cliff level. Insome embodiments, the one or more documents are made unavailable byremoving them from the corpus. Step 612 of process 600 comprises theoption of eliminating evidence fragments related to the documents madeunavailable.

Step 614 of process 600 comprises reperforming the answer generationusing the reduced number of evidence fragments and related documents.Step 616 of process 600 comprises generating a second list of documents.In some embodiments, the second list of documents is labeled A′₁ toA′_(n). In some embodiments a new confidence cliff value is determined,using the regenerated answer, in the same manner as performed in step608, and is labeled A′_(k).

In step 618 process 600 determines whether there is a change in the listof documents above the confidence cliff for the document A₁ to A_(n) andthe documents A′₁ to A′_(n). If that is the case, the algorithmterminates and proceeds to step 620. Otherwise, the process loopsback—case “n”—to the step 608 of computing the confidence cliff A_(k).

In step 620, process 600 outputs, as answers, evidence fragments therewere the highest confidence level and stores all documents above theconfidence cliff together with a reduced list of evidence fragments aswell as the question and the answer.

However, as previously mentioned, instead of storing the documents, onlymetadata of the documents may be stored. Additionally, only a fractionof the evidence fragments—e.g., the topmost 1% of evidence fragments upto an exemplary 10% of the evidence fragments—may be stored. Bothactivities help to reduce the amount of stored data (archived data)significantly.

FIG. 7 shows a simplified block diagram of a cognitive system 700 forarchiving of topmost ranked documents of a result of a query against acognitive system 700. The cognitive system 700 comprises at least onecognitive engine 702, a search engine 704, a plurality of storeddocuments 706, and at least one related learned model 708. The cognitivesystem 700 comprises a result determination unit 710 adapted fordetermining, for the result of a first query against the cognitivesystem 700 based on the at least one related learned model 708, aplurality of evidence fragments, where the result determination unit 710is further adapted for determining, as the result of the first query, afirst list of documents out of the plurality of stored documents, wherethe documents in the first list relate to the plurality of determinedevidence fragments.

The cognitive system 700 also comprises a metadata determination unit712 adapted for determining metadata of the documents of the first listof documents, and a removal unit 714 adapted for removing a documentfrom the stored documents, where the removed document is an element ofthe resulting first list of documents, and where the document does notrelate to topmost ranked evidence fragments. The result determinationunit 710 is also adapted for redetermining as second result a secondlist of documents of the first query out of the plurality of storeddocuments without the removed document, where the documents in the firstand second list relate to the plurality of determined evidencefragments.

A comparison module 716 is adapted for comparing the first list ofdocuments with the second list of documents, and the removal unit 714 isalso adapted for, upon determining identical documents in the comparedfirst and second list of documents up to a confidence cliff, removinganother document from the stored documents, where the removed otherdocument is an element of the resulting list of documents, and where theother document does not relate to the topmost ranked evidence fragments.

A loop unit 718 is adapted for triggering the removal unit 714, thedetermination unit 710 for the redetermination and the comparison module716 as along as said first list of documents and said second list ofdocuments are equal up to said confidence cliff and documents remain insaid stored documents. The cognitive system 700 also comprises a storagemodule 720 adapted for storing metadata of the documents of the firstlist, the plurality of evidence fragments, and the first query.

Embodiments of the present disclosure can be implemented together withvirtually any type of computer, regardless of the platform beingsuitable for storing and/or executing program code. FIG. 8 shows, as anexample, a computing system 800 suitable for executing program coderelated to the proposed method.

The computing system 800 is only one example of a suitable computersystem and is not intended to suggest any limitation as to the scope ofuse or functionality of embodiments of the present disclosure describedherein, regardless, whether the computer system 800 is capable of beingimplemented and/or performing any of the functionality set forthhereinabove. In computer system 800, there are components, which areoperational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that can besuitable for use with computer system/server 800 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like. Computersystem/server 800 can be described in the general context of computersystem-executable instructions, such as program modules, being executedby a computer system. Generally, program modules can include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.Computer system/server 800 can be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules can be locatedin both, local and remote computer system storage media, includingmemory storage devices.

As shown in the figure, computer system/server 800 is shown in the formof a general-purpose computing device. The components of computersystem/server 800 can include, but are not limited to, one or moreprocessors or processing units 802, a system memory 804, and a bus 806that couple various system components including system memory 804 to theprocessor 802. Bus 806 represents one or more of any of several types ofbus structures, including a memory bus or memory controller, aperipheral bus, an accelerated graphics port, and a processor or localbus using any of a variety of bus architectures. By way of example, andnot limiting, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus. Computer system/server 800typically includes a variety of computer system readable media. Suchmedia can be any available media that is accessible by computersystem/server 800, and it includes both volatile and non-volatile media,and removable and non-removable media.

The system memory 804 can include computer system readable media in theform of volatile memory, such as random access memory (RAM) 808 and/orcache memory 810. Computer system/server 800 can further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, a storage system 812 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a ‘hard drive’). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a ‘floppy disk’), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 806 by one or more datamedia interfaces. As will be further depicted and described below,memory 804 can include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the present disclosure.

The program/utility, having a set (at least one) of program modules 816,can be stored in memory 804 by way of example, and not limiting, as wellas an operating system, one or more application programs, other programmodules, and program data. Each of the operating systems, one or moreapplication programs, other program modules, and program data or somecombination thereof, can include an implementation of a networkingenvironment. Program modules 816 generally carry out the functionsand/or methodologies of embodiments of the present disclosure, asdescribed herein.

The computer system/server 800 can also communicate with one or moreexternal devices 818 such as a keyboard, a pointing device, a display820, etc.; one or more devices that enable a user to interact withcomputer system/server 800; and/or any devices (e.g., network card,modem, etc.) that enable computer system/server 800 to communicate withone or more other computing devices. Such communication can occur viaInput/Output (I/O) interfaces 814. Still yet, computer system/server 800can communicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 822. As depicted, network adapter 822can communicate with the other components of computer system/server 800via bus 806. It should be understood that, although not shown, otherhardware and/or software components could be used in conjunction withcomputer system/server 800. Examples include, but are not limited to:microcode, device drivers, redundant processing units, external diskdrive arrays, RAID systems, tape drives, and data archival storagesystems, etc.

Additionally, the cognitive system 700 for archiving of topmost rankeddocuments of a result of a query against a cognitive system can beattached to the bus system 806.

The descriptions of the various embodiments of the present disclosurehave been presented for purposes of illustration but are not intended tobe exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinaryskills in the art without departing from the scope and spirit of thedescribed embodiments. The terminology used herein was chosen to bestexplain the principles of the embodiments, the practical application ortechnical improvement over technologies found in the marketplace, or toenable others of ordinary skills in the art to understand theembodiments disclosed herein.

The present disclosure can be embodied as a system, a method, and/or acomputer program product. The computer program product can include acomputer readable storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outaspects of the present disclosure.

The medium can be an electronic, magnetic, optical, electromagnetic,infrared or a semi-conductor system for a propagation medium. Examplesof a computer-readable medium can include a semi-conductor orsolid-state memory, magnetic tape, a removable computer diskette, arandom access memory (RAM), a read-only memory (ROM), a rigid magneticdisk and an optical disk. Current examples of optical disks includecompact disk-read only memory (CD-ROM), compact disk-read/write(CD-R/W), DVD and Blu-Ray-Disk.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium can be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disk read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure can be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including anobject-oriented programming language such as Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions can execute entirely on the user'scomputer, partly on the user's computer as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer can be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection can be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) can execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thepresent disclosure. It will be understood that each block of theflowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer readable program instructions.

These computer readable program instructions can be provided to aprocessor of a general-purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionscan also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions can also be loaded onto acomputer, other programmable data processing apparatuses, or anotherdevice to cause a series of operational steps to be performed on thecomputer, other programmable apparatus or other device to produce acomputer implemented process, such that the instructions which executeon the computer, other programmable apparatuses, or another deviceimplement the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowcharts and/or block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block can occur out of theorder noted in the figures. For example, two blocks shown in successioncan, in fact, be executed substantially concurrently, or the blocks cansometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or act or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to limit the present disclosure. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will further be understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or steps plus function elements in the claims below are intendedto include any structure, material, or act for performing the functionin combination with other claimed elements, as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description but is not intended to be exhaustive orlimited to the present disclosure in the form disclosed. Manymodifications and variations will be apparent to those of ordinaryskills in the art without departing from the scope and spirit of thepresent disclosure. The embodiments are chosen and described in order tobest explain the principles of the present disclosure and the practicalapplication, and to enable others of ordinary skills in the art tounderstand the present disclosure for various embodiments with variousmodifications, as are suited to the particular use contemplated.

What is claimed is:
 1. A computer-implemented method for archiving oftopmost ranked documents, said method comprising: receiving a firstquery into a cognitive system, wherein said cognitive system comprises acognitive engine, a plurality of stored documents, and a related learnedmodel; determining a first result of said first query against saidcognitive system based on said related learned model, wherein said firstresult comprises a plurality of evidence fragments; ranking saidplurality of evidence fragments; determining for said first query afirst list of documents comprising documents selected from saidplurality of stored documents, wherein respective documents in saidfirst list of documents relate to said plurality of evidence fragments;determining metadata of said documents in said first list of documents;removing a first document from said plurality of stored documents,wherein said first document is an element of said first list ofdocuments, and wherein said first document does not relate to topmostranked evidence fragments; redetermining a second result comprising asecond list of documents of said first query of said plurality of storeddocuments without said first document, wherein said documents in saidsecond list relate to said plurality of evidence fragments; determininga confidence cliff; comparing said first list of documents with saidsecond list of documents; in response to determining identical documentsin said first list of documents and said second list of documents up tosaid confidence cliff, removing a second document from said plurality ofstored documents, wherein said second document is an element of saidfirst list of documents and said second list of documents, and whereinsaid second document does not relate to said topmost ranked evidencefragments; and storing said metadata of respective documents of saidfirst list of documents, said plurality of evidence fragments, and saidfirst query.
 2. The method according to claim 1, further comprising:while said first list of documents and said second list of documents areequal up to said confidence cliff and respective documents in each listremain in said plurality of stored documents, repeating said step ofremoving a subsequent document, said step of redetermining a subsequentresult, and said step of comparing said first list of documents and saidsecond list of documents.
 3. The method according to claim 1, whereinsaid related learned model is agnostic to a missing document in saidplurality of stored documents.
 4. The method according to claim 1,wherein said metadata comprises at least one piece of informationselected from the group consisting of: a document name, a documentauthor, a document source, a document publishing date, documentbibliographic data, an International Standard Book Number, and aweb-link.
 5. The method according to claim 1, further comprising storinginformation about said related learned model and version information ofsaid cognitive engine used in said first query.
 6. The method accordingto claim 1, wherein said storing of said plurality of evidence fragmentsfurther comprises storing only said topmost evidence fragments of saidplurality of evidence fragments.
 7. The method according to claim 1,wherein said confidence cliff is a predefined number of documents. 8.The method according to claim 1, wherein said confidence cliff relatesto a document in said first list of documents with a predefinedconfidence level.
 9. The method according to claim 1, wherein saidconfidence cliff is determined by: determining a confidence levelpolynomial using absolute values of confidence levels of documents ofsaid first list of documents; and setting said confidence cliff to saiddocument after a first local maximum of said confidence levelpolynomial.
 10. The method according to claim 1, wherein a list ofanswers relating to said first query is determined by said cognitiveengine together with a scoring of said list of answers.
 11. A system forarchiving of topmost ranked documents comprising: a processor; and acomputer-readable storage medium communicatively coupled to theprocessor and storing program instructions which, when executed by theprocessor, are configured to cause the processor to perform a methodcomprising: receiving a first query into a cognitive system, whereinsaid cognitive system comprises a cognitive engine, a plurality ofstored documents, and a related learned model; determining a firstresult of said first query against said cognitive system based on saidrelated learned model, wherein said first result comprises a pluralityof evidence fragments; ranking said plurality of evidence fragments;determining for said first query a first list of documents comprisingdocuments selected from said plurality of stored documents, whereinrespective documents in said first list of documents relate to saidplurality of evidence fragments; determining metadata of said documentsin said first list of documents; removing a first document from saidplurality of stored documents, wherein said first document is an elementof said first list of documents, and wherein said first document doesnot relate to topmost ranked evidence fragments; redetermining a secondresult comprising a second list of documents of said first query of saidplurality of stored documents without said first document, wherein saiddocuments in said second list relate to said plurality of evidencefragments; determining a confidence cliff; comparing said first list ofdocuments with said second list of documents; in response to determiningidentical documents in said first list of documents and said second listof documents up to said confidence cliff, removing a second documentfrom said plurality of stored documents, wherein said second document isan element of said first list of documents and said second list ofdocuments, and wherein said second document does not relate to saidtopmost ranked evidence fragments; and storing said metadata ofrespective documents of said first list of documents, said plurality ofevidence fragments, and said first query.
 12. The system according toclaim 11, wherein said related learned model is agnostic to a missingdocument in said plurality of stored documents.
 13. The system accordingto claim 11, wherein said metadata comprises at least one piece ofinformation selected from the group consisting of: a document name, adocument author, a document source, a document publishing date, documentbibliographic data, an International Standard Book Number, and aweb-link.
 14. The system according to claim 11, the program instructionsare further configured to cause the processor to perform a methodfurther comprising storing information about said related learned modeland version information of said cognitive engine used in said firstquery.
 15. The system according to claim 11, wherein the programinstructions are further configured to cause the processor to perform amethod further comprising storing only said topmost evidence fragmentsof said plurality of evidence fragments.
 16. The system according toclaim 11, wherein said confidence cliff is a predefined number ofdocuments.
 17. The system according to claim 11, wherein said confidencecliff relates to a document in said first list of documents with apredefined confidence level.
 18. The system according to claim 11,wherein said confidence cliff is determined by: determining a confidencelevel polynomial using absolute values of confidence levels of documentsof said first list of documents; and setting said confidence cliff tosaid document after a first local maximum of said confidence levelpolynomial.
 19. The system according to claim 11, wherein said cognitiveengine is adapted for determining a list of answers relating to saidfirst query together with a scoring of said list of answers.
 20. Acomputer program product for archiving of topmost ranked documents, thecomputer program product comprising a computer readable storage mediumhaving program instructions embodied therewith, wherein the computerreadable storage medium is not a transitory signal per se, the programinstructions executable by a processor to cause the processor to performa method comprising: receiving a first query into a cognitive system,wherein said cognitive system comprises a cognitive engine, a pluralityof stored documents, and a related learned model; determining a firstresult of said first query against said cognitive system based on saidrelated learned model, wherein said first result comprises a pluralityof evidence fragments; ranking said plurality of evidence fragments;determining for said first query a first list of documents comprisingdocuments selected from said plurality of stored documents, whereinrespective documents in said first list of documents relate to saidplurality of evidence fragments; determining metadata of said documentsin said first list of documents; removing a first document from saidplurality of stored documents, wherein said first document is an elementof said first list of documents, and wherein said first document doesnot relate to topmost ranked evidence fragments; redetermining a secondresult comprising a second list of documents of said first query of saidplurality of stored documents without said first document, wherein saiddocuments in said second list relate to said plurality of evidencefragments; determining a confidence cliff; comparing said first list ofdocuments with said second list of documents; in response to determiningidentical documents in said first list of documents and said second listof documents up to said confidence cliff, removing a second documentfrom said plurality of stored documents, wherein said second document isan element of said first list of documents and said second list ofdocuments, and wherein said second document does not relate to saidtopmost ranked evidence fragments; and storing said metadata ofrespective documents of said first list of documents, said plurality ofevidence fragments, and said first query.